Enhancing Accuracy for Fingerprint-based Indoor Localization

PhD Thesis Proposal Defence


Title: "Enhancing Accuracy for Fingerprint-based Indoor Localization"

by

Mr. Suining HE


Abstract:

The commercial potential of indoor location-based services (ILBS) has 
spurred recent development of many indoor positioning techniques. Among 
all the signals proposed for indoor positioning, Wi-Fi emerges as a 
promising and cost-effective one due to the pervasive deployment of 
wireless LANs (WLANs). Wi-Fi fingerprinting has attracted much attention 
recently because it does not require line-of-sight measurement from access 
points (APs), and has high applicability in complex indoor environment.

Offering quality ILBS requires accurate indoor positioning.  In this 
thesis, we study several approaches to make Wi-Fi fingerprinting highly 
accurate.  The approaches are to mitigate noisy signal measurement, to 
fuse distance sensor with fingerprinting, and to adaptively learn 
fingerprint patterns over time.  We will conduct extensive experimental 
studies to validate the performance of the approaches.

Previous fingerprinting positioning based on certain similarity metric 
often suffers from ambiguous matching problem of reference points, 
resulting in high decision uncertainty. To address this, we propose a 
novel approach based on junction of signal tiles, which are formed based 
on the first two moments of the signals. The target location is then 
constrained within the junction area. This overcomes position ambiguity 
problem and achieves highly accurate positioning.

To further enhance localization accuracy, we study how to fuse fingerprint 
with distance information. Our approach is applicable to a wide range of 
sensors (peer-assisted, inertial navigation sensor, etc.) and wireless 
fingerprints (Wi-Fi, Bluetooth, etc.). By a novel optimization formulation 
which jointly fuses distance bounds and measured fingerprint signals, it 
achieves low positioning errors even under complex indoor environment.

Fingerprinting accuracy deteriorates if the AP signals are altered (due to 
AP movement, partitioning, etc.).  We propose and study a novel 
clustering-based scheme which can localize targets despite AP alteration, 
and can identify the altered APs. Using a novel Gaussian process, our 
algorithm can also adapt the fingerprint map to the altered signal 
environment.


Date:			Friday, 26 February 2016

Time:                  	10:00am - 12:00noon

Venue:                  CYTG001
                         CYT Building

Committee Members:	Prof. Gary Chan (Supervisor)
  			Dr. Pan Hui (Chairperson)
 			Dr. Qiong Luo
  			Dr. Raymond Wong


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